A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems

Abstract

Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State–Action–Reward–State–Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.

Binarization of Metaheuristics: Is the Transfer Function Really Important?

“…While some MHs operate on binary domains without a binary scheme, studies have demonstrated that continuous MHs supported by a binary scheme perform exceptionally well on multiple NP-hard combinatorial problems [ 1 ]. Examples of such MHs include the binary bat algorithm [ 28 , 29 ], particle swarm optimization [ 30 ], binary sine cosine algorithm [ 2 , 31 , 32 , 33 ], binary salp swarm algorithm [ 34 , 35 ], binary grey wolf optimizer [ 32 , 36 , 37 ], binary dragonfly algorithm [ 38 , 39 ], the binary whale optimization algorithm [ 2 , 32 , 40 ], and the binary magnetic optimization algorithm [ 41 ].…”

“…In the literature, there are several related works on binarization [ 30 , 42 ] that have laid the groundwork for investigations into this domain problem, as there are several practical applications where working in binary domains is necessary. Moreover, research has emerged on how the change of binarization schemes affects each iteration of the search process, such as time-varying binarization schemes [ 38 ] or binarization scheme selectors [ 2 , 32 , 60 ], where the influence of binarization schemes and their impact at both the problem level and each iteration of the search has been demonstrated.…”

Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics

“…Binary combinatorial problems, such as the Set Covering Problem [9,11,77,104,105], Knapsack Problem [109,110], or Cell Formation Problem [106], are increasingly common in the industry. Given the demand for good results in reasonable times, metaheuristics have begun to gain ground as resolution techniques.…”

“…The No Free Lunch (NFL) theorem [47][48][49] indicates that there is no optimization algorithm capable of solving all existing optimization problems effectively. This is the primary motivation behind binarizing continuous metaheuristics, as evident in the literature where authors have presented binary versions for the Bat Algorithm [74,75], Particle Swarm Optimization [76], Sine Cosine Algorithm [10,11,77,78], Salp Swarm Algorithm [79,80], Grey Wolf Optimizer [11,81,82], Dragonfly Algorithm [83,84], Whale Optimization Algorithm [11,77,85], and Magnetic Optimization Algorithm [86].…”

“…The study of metaheuristics has grown in recent years, with hybridizations emerging as the current trend. There exist hybridizations between metaheuristics such as those proposed in [2][3][4][5], hyperheuristic approaches where a high-level metaheuristic guides another low-level one [6][7][8], approaches where machine learning techniques enhance metaheuristics [9][10][11], and other approaches in which chaos theory is utilized to modify the stochastic behavior of metaheuristics [12][13][14][15].…”